Insight and algorithmic clustering for automated synthesis

ABSTRACT

A decision support system and method, which receives user inputs comprising: at least one user criterion, and at least one user input tuning parameter representing user tradeoff preferences for producing an output; and selectively produces an output of tagged data from a clustered database in dependence on the at least one user criterion, the at least one user input tuning parameter, and a distance function; receives at least one reference-user input parameter representing the at least one reference-user&#39;s analysis of the tagged data and the corresponding user inputs, to adapt the distance function in accordance with the reference-user inputs as a feedback signal; and clusters the database in dependence on at least the distance function, wherein the reference-user acts to optimize the distance function based on the user inputs and the output, and on at least one reference-user inference.

CROSS REFERENCE TO RELATED APPLICATIONS

The present application is a:

Continuation of U.S. patent application Ser. No. 15/470,298, filed Mar.27, 2017, now U.S. Pat. No. 10,318,503, issued Jun. 11, 2019, which is a

Continuation of U.S. patent application Ser. No. 15/148,877, filed May6, 2016, now U.S. Pat. No. 9,607,023, issued Mar. 28, 2017, which is a

Continuation of U.S. patent application Ser. No. 13/826,338, filed Mar.14, 2013, now U.S. Pat. No. 9,336,302, issued May 10, 2016, which is a

Non-provisional and claims benefit of priority under 35 U.S.C. § 119(e)from U.S. Provisional Application No. 61/673,914, filed Jul. 20, 2012,

the entirety of which are each expressly incorporated herein byreference in their entirety.

FIELD OF THE INVENTION

The present invention relates to the field of domain or context-specificautomated data classification or truth-seeking, and more particularly tothe field of semi-supervised data clustering.

BACKGROUND OF THE INVENTION

Investment Insights

Understanding the true value of assets of any kind is a critical problemin purchase decisions. Often, the available markets are inefficient atfinding the true value (appropriate risk adjusted return) of the assets.As such, it is difficult for an investor or potential investor to make,monitor and communicate decisions around comparison of the value of twoassets, comparing the value of two different asset types, and comparingthe value of an asset to the market of all such assets.

Most types of assets have a price (as determined by what you pay for it)and a value (what it is actually worth). If you paid the right price,you would realize the expected value. Value is reflective of the pricein relation to the risk-adjusted return. In poorly valued items, theprice is subject to arbitrage. As the market becomes more efficient, theprice and value should converge. Duration is the time horizon betweenwhen an interest in an asset is acquired as a primary and when sale,transfer, or the like realizes the complete value, e.g., in a completelymeasurable asset such as treasury cash, e.g., by sale, transfer, or thelike. As the duration is increased, a secondary market or exchange maybe available to trade the asset, and speculative investors may take partseeking to profit off changes in risk or reward over time, and often notfocusing on the true value.

While there is much emphasis on data correctness, using incomplete datacan also lead to faulty analysis. Overall completeness consists of thevarious completeness factors being monitored, as well as the timelinessof the data. Coverage refers to the percent of the total universecovered by a dataset and the expanse (or breadth) of the coverage.Proper coverage metrics must address the issue of whether a particulardata set is indeed a representative sample of the space, such thatanalysis run on the data set gives results that are consistent with thesame analysis on the full universe. Correctness, completeness andcoverage are a function of the statistical confidence interval that isrequired by the user on the results. User may trade-off confidence toincrease other parameters.

When investing in assets, each with its own risk and reward profile, itis important for an investor to understand what risks (including pricingrisk) are being incurred, what return on investment is to be expected,what term/duration of investment is considered, and how a hypotheticalefficient market would value the asset. In terms of market valuation(especially in an inefficient market that does not correctly priceassets), it is important to compare an asset to three potentialdifferent valuation anchors, its “peers”, or assets which have similarrisks vs. returns to evaluate which provides superior value (orexplained and accounted for differences in risk and reward profiles),the overall market benchmark (i.e. the largest universe of peers thatrepresent the appropriate market) and potentially a broader category ofpeers from other markets (or clusters). In some cases, theidentification of the peer group may be difficult, and not readilyamenable to accurate fully automated determination. Humans, however, mayhave insights that can resolve this issue.

Some users may have superior insights into understanding the price,risks and rewards, and thus a better assessment of the value or riskadjusted return than others. Investors seek benefit of those withsuperior insights as advisors. Those with superior insights canrecognize patterns that others might not see, and classify data and drawabstractions and conclusions differently than others. However,prospectively determining which advisor(s) to rely on in terms ofsuperior return on investment while incurring acceptable risk remains anunresolved problem.

Data Clustering

Data clustering is a process of grouping together data points havingcommon characteristics. In automated processes, a cost function ordistance function is defined, and data is classified is belonging tovarious clusters by making decisions about its relationship to thevarious defined clusters (or automatically defined clusters) inaccordance with the cost function or distance function. Therefore, theclustering problem is an automated decision-making problem. The scienceof clustering is well established, and various different paradigms areavailable. After the cost or distance function is defined and formulatedas clustering criteria, the clustering process becomes one ofoptimization according to an optimization process, which itself may beimperfect or provide different optimized results in dependence on theparticular optimization employed. For large data sets, a completeevaluation of a single optimum state may be infeasible, and thereforethe optimization process subject to error, bias, ambiguity, or otherknown artifacts.

In some cases, the distribution of data is continuous, and the clusterboundaries sensitive to subjective considerations or have particularsensitivity to the aspects and characteristics of the clusteringtechnology employed. In contrast, in other cases, the inclusion of datawithin a particular cluster is relatively insensitive to the clusteringmethodology. Likewise, in some cases, the use of the clustering resultsfocuses on the marginal data, that is, the quality of the clustering isa critical factor in the use of the system.

The ultimate goal of clustering is to provide users with meaningfulinsights from the original data, so that they can effectively solve theproblems encountered. Clustering acts to effectively reduce thedimensionality of a data set by treating each cluster as a degree offreedom, with a distance from a centroid or other characteristicexemplar of the set. In a non-hybrid system, the distance is a scalar,while in systems that retain some flexibility at the cost of complexity,the distance itself may be a vector. Thus, a data set with 10,000 datapoints, potentially has 10,000 degrees of freedom, that is, each datapoint represents the centroid of its own cluster. However, if it isclustered into 100 groups of 100 data points, the degrees of freedom isreduced to 100, with the remaining differences expressed as a distancefrom the cluster definition. Cluster analysis groups data objects basedon information in or about the data that describes the objects and theirrelationships. The goal is that the objects within a group be similar(or related) to one another and different from (or unrelated to) theobjects in other groups. The greater the similarity (or homogeneity)within a group and the greater the difference between groups, the“better” or more distinct is the clustering.

In some cases, the dimensionality may be reduced to one, in which caseall of the dimensional variety of the data set is reduced to a distanceaccording to a distance function. This distance function may be useful,since it permits dimensionless comparison of the entire data set, andallows a user to modify the distance function to meet variousconstraints. Likewise, in certain types of clustering, the distancefunctions for each cluster may be defined independently, and thenapplied to the entire data set. In other types of clustering, thedistance function is defined for the entire data set, and is not (orcannot readily be) tweaked for each cluster. Similarly, feasibleclustering algorithms for large data sets preferably do not haveinteractive distance functions in which the distance function itselfchanges depending on the data. Many clustering processes are iterative,and as such produce a putative clustering of the data, and then seek toproduce a better clustering, and when a better clustering is found,making that the putative clustering. However, in complex data sets,there are relationships between data points such that a cost or penalty(or reward) is incurred if data points are clustered in a certain way.Thus, while the clustering algorithm may split data points which have anaffinity (or group together data points, which have a negative affinity,the optimization becomes more difficult.

Thus, for example, a semantic database may be represented as a set ofdocuments with words or phrases. Words may be ambiguous, such as“apple”, representing a fruit, a computer company, a record company, anda musical artist. In order to effectively use the database, the multiplemeanings or contexts need to be resolved. In order to resolve thecontext, an automated process might be used to exploit availableinformation for separating the meanings, i.e., clustering documentsaccording to their context. This automated process can be difficult asthe data set grows, and in some cases the available information isinsufficient for accurate automated clustering. On the other hand, ahuman can often determine a context by making an inference, which,though subject to error or bias, may represent a most useful resultregardless.

In supervised classification, the mapping from a set of input datavectors to a finite set of discrete class labels is modeled in terms ofsome mathematical function including a vector of adjustable parameters.The values of these adjustable parameters are determined (optimized) byan inductive learning algorithm (also termed inducer), whose aim is tominimize an empirical risk function on a finite data set of input. Whenthe inducer reaches convergence or terminates, an induced classifier isgenerated. In unsupervised classification, called clustering orexploratory data analysis, no labeled data are available. The goal ofclustering is to separate a finite unlabeled data set into a finite anddiscrete set of “natural,” hidden data structures, rather than providean accurate characterization of unobserved samples generated from thesame probability distribution. In semi-supervised classification, aportion of the data are labeled, or sparse label feedback is used duringthe process.

Non-predictive clustering is a subjective process in nature, seeking toensure that the similarity between objects within a cluster is largerthan the similarity between objects belonging to different clusters.Cluster analysis divides data into groups (clusters) that aremeaningful, useful, or both. If meaningful groups are the goal, then theclusters should capture the “natural” structure of the data. In somecases, however, cluster analysis is only a useful starting point forother purposes, such as data summarization. However, this often begs thequestion, especially in marginal cases; what is the natural structure ofthe data, and how do we know when the clustering deviates from “truth”?

Many data analysis techniques, such as regression or principal componentanalysis (PCA), have a time or space complexity of O(m²) or higher(where m is the number of objects), and thus, are not practical forlarge data sets. However, instead of applying the algorithm to theentire data set, it can be applied to a reduced data set consisting onlyof cluster prototypes. Depending on the type of analysis, the number ofprototypes, and the accuracy with which the prototypes represent thedata, the results can be comparable to those that would have beenobtained if all the data could have been used. The entire data set maythen be assigned to the clusters based on a distance function.

Clustering algorithms partition data into a certain number of clusters(groups, subsets, or categories). Important considerations includefeature selection or extraction (choosing distinguishing or importantfeatures, and only such features); Clustering algorithm design orselection (accuracy and precision with respect to the intended use ofthe classification result; feasibility and computational cost; etc.);and to the extent different from the clustering criterion, optimizationalgorithm design or selection.

Finding nearest neighbors can require computing the pairwise distancebetween all points. However, clusters and their cluster prototypes mightbe found more efficiently. Assuming that the clustering distance metricreasonably includes close points, and excludes far points, then theneighbor analysis may be limited to members of nearby clusters, thusreducing the complexity of the computation.

There are generally three types of clustering structures, known aspartitional clustering, hierarchical clustering, and individualclusters. The most commonly discussed distinction among different typesof clusterings is whether the set of clusters is nested or unnested, orin more traditional terminology, hierarchical or partitional. Apartitional clustering is simply a division of the set of data objectsinto non-overlapping subsets (clusters) such that each data object is inexactly one subset. If the clusters have sub-clusters, then we obtain ahierarchical clustering, which is a set of nested clusters that areorganized as a tree. Each node (cluster) in the tree (except for theleaf nodes) is the union of its children (sub-clusters), and the root ofthe tree is the cluster containing all the objects. Often, but notalways, the leaves of the tree are singleton clusters of individual dataobjects. A hierarchical clustering can be viewed as a sequence ofpartitional clusterings and a partitional clustering can be obtained bytaking any member of that sequence; i.e., by cutting the hierarchicaltree at a particular level.

There are many situations in which a point could reasonably be placed inmore than one cluster, and these situations are better addressed bynon-exclusive clustering. In the most general sense, an overlapping ornon-exclusive clustering is used to reflect the fact that an object cansimultaneously belong to more than one group (class). A non-exclusiveclustering is also often used when, for example, an object is “between”two or more clusters and could reasonably be assigned to any of theseclusters. In a fuzzy clustering, every object belongs to every clusterwith a membership weight. In other words, clusters are treated as fuzzysets. Similarly, probabilistic clustering techniques compute theprobability with which each point belongs to each cluster.

In many cases, a fuzzy or probabilistic clustering is converted to anexclusive clustering by assigning each object to the cluster in whichits membership weight or probability is highest. Thus, the inter-clusterand intra-cluster distance function is symmetric. However, it is alsopossible to apply a different function to uniquely assign objects to aparticular cluster.

A well-separated cluster is a set of objects in which each object iscloser (or more similar) to every other object in the cluster than toany object not in the cluster. Sometimes a threshold is used to specifythat all the objects in a cluster must be sufficiently close (orsimilar) to one another. The distance between any two points indifferent groups is larger than the distance between any two pointswithin a group. Well-separated clusters do not need to be spherical, butcan have any shape.

If the data is represented as a graph, where the nodes are objects andthe links represent connections among objects, then a cluster can bedefined as a connected component; i.e., a group of objects that aresignificantly connected to one another, but that have less connected toobjects outside the group. This implies that each object in acontiguity-based cluster is closer to some other object in the clusterthan to any point in a different cluster.

A density-based cluster is a dense region of objects that is surroundedby a region of low density. A density-based definition of a cluster isoften employed when the clusters are irregular or intertwined, and whennoise and outliers are present. DBSCAN is a density-based clusteringalgorithm that produces a partitional clustering, in which the number ofclusters is automatically determined by the algorithm. Points inlow-density regions are classified as noise and omitted; thus, DBSCANdoes not produce a complete clustering.

A prototype-based cluster is a set of objects in which each object iscloser (more similar) to the prototype that defines the cluster than tothe prototype of any other cluster. For data with continuous attributes,the prototype of a cluster is often a centroid, i.e., the average (mean)of all the points in the cluster. When a centroid is not meaningful,such as when the data has categorical attributes, the prototype is oftena medoid, i.e., the most representative point of a cluster. For manytypes of data, the prototype can be regarded as the most central point.These clusters tend to be globular. K-means is a prototype-based,partitional clustering technique that attempts to find a user-specifiednumber of clusters (K), which are represented by their centroids.Prototype-based clustering techniques create a one-level partitioning ofthe data objects. There are a number of such techniques, but two of themost prominent are K-means and K-medoid. K-means defines a prototype interms of a centroid, which is usually the mean of a group of points, andis typically applied to objects in a continuous n-dimensional space.K-medoid defines a prototype in terms of a medoid, which is the mostrepresentative point for a group of points, and can be applied to a widerange of data since it requires only a proximity measure for a pair ofobjects. While a centroid almost never corresponds to an actual datapoint, a medoid, by its definition, must be an actual data point.

In the K-means clustering technique, we first choose K initialcentroids, the number of clusters desired. Each point in the data set isthen assigned to the closest centroid, and each collection of pointsassigned to a centroid is a cluster. The centroid of each cluster isthen updated based on the points assigned to the cluster. We iterativelyassign points and update until convergence (no point changes clusters),or equivalently, until the centroids remain the same. For somecombinations of proximity functions and types of centroids, K-meansalways converges to a solution; i.e., K-means reaches a state in whichno points are shifting from one cluster to another, and hence, thecentroids don't change. Because convergence tends to b asymptotic, theend condition may be set as a maximum change between iterations. Becauseof the possibility that the optimization results in a local minimuminstead of a global minimum, errors may be maintained unless and untilcorrected. Therefore, a human assignment or reassignment of data pointsinto classes, either as a constraint on the optimization, or as aninitial condition, is possible.

To assign a point to the closest centroid, a proximity measure isrequired. Euclidean (L2) distance is often used for data points inEuclidean space, while cosine similarity may be more appropriate fordocuments. However, there may be several types of proximity measuresthat are appropriate for a given type of data. For example, Manhattan(L1) distance can be used for Euclidean data, while the Jaccard measureis often employed for documents. Usually, the similarity measures usedfor K-means are relatively simple since the algorithm repeatedlycalculates the similarity of each point to each centroid, and thuscomplex distance functions incur computational complexity. Theclustering may be computed as a statistical function, e.g., mean squareerror of the distance of each data point according to the distancefunction from the centroid. Note that the K-means may only find a localminimum, since the algorithm does not test each point for each possiblecentroid, and the starting presumptions may influence the outcome. Thetypical distance functions for documents include the Manhattan (L1)distance, Bregman divergence, Mahalanobis distance, squared Euclideandistance and cosine similarity.

An optimal clustering will be obtained as long as two initial centroidsfall anywhere in a pair of clusters, since the centroids willredistribute themselves, one to each cluster. As the number of clustersincreases, it is increasingly likely that at least one pair of clusterswill have only one initial centroid, and because the pairs of clustersare further apart than clusters within a pair, the K-means algorithmwill not redistribute the centroids between pairs of clusters, leadingto a suboptimal local minimum. One effective approach is to take asample of points and cluster them using a hierarchical clusteringtechnique. K clusters are extracted from the hierarchical clustering,and the centroids of those clusters are used as the initial centroids.This approach often works well, but is practical only if the sample isrelatively small, e.g., a few hundred to a few thousand (hierarchicalclustering is expensive), and K is relatively small compared to thesample size. Other selection schemes are also available.

The space requirements for K-means are modest because only the datapoints and centroids are stored. Specifically, the storage required isO((m+K)^(n)), where m is the number of points and n is the number ofattributes. The time requirements for K-means are also modest-basicallylinear in the number of data points. In particular, the time required isO(I×K×m×n), where I is the number of iterations required forconvergence. As mentioned, I is often small and can usually be safelybounded, as most changes typically occur in the first few iterations.Therefore, K-means is linear in m, the number of points, and isefficient as well as simple provided that K, the number of clusters, issignificantly less than m.

Outliers can unduly influence the clusters, especially when a squarederror criterion is used. However, in some clustering applications, theoutliers should not be eliminated or discounted, as their appropriateinclusion may lead to important insights. In some cases, such asfinancial analysis, apparent outliers, e.g., unusually profitableinvestments, can be the most interesting points.

Hierarchical clustering techniques are a second important category ofclustering methods. There are two basic approaches for generating ahierarchical clustering: Agglomerative and divisive. Agglomerativeclustering merges close clusters in an initially high dimensionalityspace, while divisive splits large clusters. Agglomerative clusteringrelies upon a cluster distance, as opposed to an object distance. Forexample, the distance between centroids or medioids of the clusters, theclosest points in two clusters, the further points in two clusters, orsome average distance metric. Ward's method measures the proximitybetween two clusters in terms of the increase in the sum of the squaresof the errors that results from merging the two clusters.

Agglomerative Hierarchical Clustering refers to clustering techniquesthat produce a hierarchical clustering by starting with each point as asingleton cluster and then repeatedly merging the two closest clustersuntil a single, all-encompassing cluster remains. Agglomerativehierarchical clustering cannot be viewed as globally optimizing anobjective function. Instead, agglomerative hierarchical clusteringtechniques use various criteria to decide locally, at each step, whichclusters should be merged (or split for divisive approaches). Thisapproach yields clustering algorithms that avoid the difficulty ofattempting to solve a hard combinatorial optimization problem.Furthermore, such approaches do not have problems with local minima ordifficulties in choosing initial points. Of course, the time complexityof O(m² log m) and the space complexity of O(m²) are prohibitive in manycases. Agglomerative hierarchical clustering algorithms tend to makegood local decisions about combining two clusters since they can useinformation about the pair-wise similarity of all points. However, oncea decision is made to merge two clusters, it cannot be undone at a latertime. This approach prevents a local optimization criterion frombecoming a global optimization criterion.

In supervised classification, the evaluation of the resultingclassification model is an integral part of the process of developing aclassification model. Being able to distinguish whether there isnon-random structure in the data is an important aspect of clustervalidation.

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SUMMARY OF THE INVENTION

The Reference-User

The present technology provides a system and method which exploits humaninteractions with an automated database system to derive insights aboutthe data structures that are difficult, infeasible, or impossible toextract in a fully automated fashion, and to use these insights toaccurately assess a risk adjusted value or cluster boundaries.

According to an aspect of the technology, the system monitors or polls aset of users, actively using the system or interacting with the outputsand providing inputs. The inputs may be normal usage, i.e., the user isacting in a goal directed manner, and providing inputs expressly relatedto the important issues, or explicit feedback, in which the user acts tocorrect or punish mistakes made by the automated system, and/or rewardor reinforce appropriate actions.

Through automated historical and action-outcome analysis, a subset ofusers, called “reference-users” are identified who demonstrate superiorinsight into the issue or sub-issue important to the system or itsusers. After the reference-users are identified, their actions or inputsare then used to modify or influence the data processing, such as toprovide values or cluster the data. The adaptive algorithm is also ableto demote reference-users to regular users. Additionally, becausereference-user status may give rise to an ability to influence markets,some degree of random promotion and demotion is employed, to lessen theincentive to exploit an actual or presumed reference-user status.Indeed, the system may employ a genetic algorithm to continuously selectappropriate reference-users, possibly through injection of “spikes” orspurious information, seeking to identify users that are able toidentify the spurious data, as an indication of users who intuitivelyunderstand the data model and its normal and expected range. Thus, thesystem is continuously or sporadically doing three things—learning fromreference-users and learning who is a reference-user, requesting moregranulation/tagging and using that learning to cluster/partition thedataset for the ordinary users for the most contextually relevantinsight.

Often, the reference-user's insights will be used to prospectivelyupdate the analytics, such as the distance function, clustering initialconditions or constraints, or optimization. However, in some cases, theadaptivity to the reference-user will only occur after verification.That is, a reference-user will provide an input which cannotcontemporaneously be verified by the automated system. That input isstored, and the correspondence of the reference-user's insight to laterreality then permits a model to be derived from that reference-userwhich is then used prospectively. This imposes a delay in the updatingof the system, but also does not reveal the reference-user's decisionsimmediately for use by others. Thus, in a financial system, areference-user might wish to withhold his insights from competitorswhile they are competitively valuable. However, after the immediatevalue has passed, the algorithm can be updated to benefit all. In aninvestment system, often a reference-user with superior insight wouldprefer that others follow, since this increases liquidity in the market,giving greater freedom to the reference-user.

A key issue is that a fully automated database analysis may be definedas an NP problem and in a massive database, the problem becomesessentially infeasible. However, humans tend to be effective patternrecognition engines, and reference-users may be selected that are betterthan average, and capable of estimating an optimal solution to a complexproblem “intuitively”, that is, without a formal and exact computation,even if computationally infeasible. As stated above, some humans arebetter than others at certain problems, and once these better ones areidentified, their insights may be exploited to advantage.

In clustering the database, a number of options are available to definethe different groups of data. One option is to define persons who have arelationship to the data. That is, instead of seeking to define thecontext as an objective difference between data, the subjectiverelationships of users to data may define the clusters. This scenarioredefines the problem from determining a cluster definition as a “topic”to determining a cluster definition as an affinity to a person. Notethat these clusters will be quite different in their content andrelationships, and thus have different application.

Optimal clustering is only one aspect of the use of a reference-user.More generally, the reference-user is a user that demonstrates uncommoninsight with respect to an issue. For example, insight may help findclusters of data that tend to gravitate toward or away from each otherand form clusters of similarity or boundaries. Clustering is at theheart of human pattern recognition, and involves informationabstraction, classification and discrimination.

Thus, according to the present technology, we consider a system having anetwork of “users”, which may be ordinary human users that simplyrequire the computer to synthesize some insight from a large dataset, aswell as “reference-users” who help the computer refine and set contextin the dataset. While the designation of user and reference-userpartitions the space of users. The process of selecting who is a userand who is a reference-user is automated and the designations may not bepersistent, i.e., the computer is continually re-evaluating who is auser and who is a reference-user based on how they interact with thesystem.

From a database user's perspective, a query should be simple, e.g.,“natural language”, and not require a specific knowledge of the datastructures within the database or a complete knowledge of the datastructures being searched. In other words, the user should not have toknow the structure of database before the query result is obtained. Theresult should preferably include relevant responses sorted or organizedaccording to relationship with the query. In other cases, the sorting orranking may be according to different criteria. Much as the clusteringproblem employs a distance function, the reporting problem also employsa ranking or information presentation prioritization function. Indeed,the outputs may be clustered either according to the clustering of thesource database, or the responses to a query may be clustered upondemand.

In some cases, a user wishes only results with high relevance, while inother cases, a user may wish to see a ranked list which extends to lowrelevance/low yield results. A list, however, is not the only way toorganize results, and, in terms of visual outputs, these may be providedas maps (see U.S. Pat. No. 7,113,958 (Three-dimensional display ofdocument set); U.S. Pat. No. 6,584,220 (Three-dimensional display ofdocument set); U.S. Pat. No. 6,484,168 (System for informationdiscovery); U.S. Pat. No. 6,772,170 (System and method for interpretingdocument contents), each of which is expressly incorporated herein byreference), three or higher dimensional representations, or otherorganizations and presentations of the data. Thus, the distinctionbetween the query or input processing, to access selected informationfrom a database, and the presentation or output processing, to presentthe data to a user, is important. In some cases, these two functions areinteractive, and for example, a context may be used preferentiallyduring presentation rather than selection.

According to one embodiment of the system and method according to thepresent technology, a reference-user is employed periodically tonormalize a data distribution, based on the reference-user's insights.This normalization acts as a correction to an automated algorithm, andthe normalization information received from the reference-user tunes thealgorithm, which, for example, represents distance function or partition(clustering) function. In effect the reference-users train the systemwhen they unconsciously partition elements from the cluster based ontheir instincts.

The system does not have to be continuously trained by thereference-user or act to continuously reselect reference-users. Thetraining is important only when the divergence between what the systemreports as insight on a self-similar cluster and what the dominant setof users consider to be an insight, becomes unacceptably large. Whenthis divergence becomes unacceptably large for the remaining users inthe network, then the reference-user training is invoked and the systemlearns from the reference-user. If the divergence corrects, the systemsstops retraining and continues as before. However, if the divergencedoes not, then the system reselects the reference-user and thenretrains. Once again if the divergence corrects, the system continues asbefore. However, if it does not, the system then flags the needs formore data by requesting additional meta-tagging of the content.

Thus, the system is continuously doing 3 things (a) learning fromreference-users; (b) learning who is a reference-user; and (c)requesting more granulation and using that learning to cluster/partitionthe dataset for the ordinary users for the most contextually relevantinsight.

Context-Based Reference-Users

Clustering of massive databases poses a number of problems. For example,the computational complexity of some algorithms is sufficiently highthat clustering cannot be updated in real time. Further, an inherentchallenge in automated clustering comes from realizing that a machinemay have no context, or that solution of the clustering problem could besignificantly facilitated by determination and exploitation of contextinformation. Thus, superior clustering in various cases requires theestablishment of context by some means to facilitate filtering of theclusters by the computer algorithm. Another aspect of this problem isthat the bases for the clustering may be determined ad hoc, or therelevant distinctions available with information provided at the time ofconsideration.

Context can be assumed if the insight required, and dataset to bealgorithmically operated on, is small and specialized enough.Unfortunately, in very high dimensionality databases, such as Google'ssemantic database of the web and related data feeds, the resultingnumber of clusters may be extraordinarily high, and as a result theclustered database may be of little use without further distinctionsbeing drawn. For example, the Google search engine requires a query, andeven then returns result based on multiple undistinguished contexts,leading to a potentially large proportion of irrelevant responses.Likewise, simplifying presumptions made to reduce complexity mayeliminate the very distinctions that are required for a particularcircumstance.

While computers have computational power for performing standardalgorithmic calculations, humans have the ability to immediately judgecontext. Humans do this contextual mapping by looking for similarity innetworks, similarity in knowledge sets and similarity in skills. Thus,an automated way of identifying how to elicit that human “secret sauce”around context, will significantly speed up the computer's ability topartition the space into proper contextually relevant clusters.

Implicit in natural language searching and “relevance” to a query is theconcept of “context”. A Boolean text search does not benefit fromknowledge of language structures and latent ambiguities, and thus willtypically deliver results that are contextually irrelevant but meet theBoolean query criteria. On the other hand, natural language searchtechnologies and other unstructured search systems can benefit fromcontext, though often determining this context requires an inference.Alternately, a user can define a context, for example by limitinghimself or herself to a special purpose database or other limitation. Auser can also seek to explicitly indicate the context, assuming that theuser is aware of the different contexts to be distinguished. However, itis often necessary to query the database before determining theclustering of responsive records, and then obtaining feedback from auser to define the context and therefore focus on respective clusters.However, in some cases the “context” that might be derived from anautomated clustering of records defies semantic description, thusrequiring a “clustering by example” feedback/training of the system, orother type of non-semantic guidance of the system, and which might incura much larger effort than most users would voluntarily endure, andperhaps incur more effort and/or higher costs than simply accepting bothrelevant and irrelevant information in response to the query anddistinguishing these manually.

The present technology proposes a solution to this problem bydesignating “reference-users”, that is, either the reference-user haspreviously indicated or proven a superior ability to operate in acertain context, or otherwise represent the context by consistency andreliability. The user context may be determined in various ways, but inthe case of persistent contexts, a user profile may be developed, and areference-user selected with whom the user has some affinity, i.e.,overlapping or correlated characteristics. There are multiple ways todesignate the reference-user—the system designates the reference-userbased on filtering a set of users to which reference-user bestrepresents the responses and preferences of the set. This designation ofreference-user comes from affinity, which could be network-affinity(users that are closely connected in the network in that context),knowledge-affinity (users that have superior expertise in that context),or skill-affinity (users possessing specialized skills in that context).

It is noted that the reference-user is discussed as an actual singlehuman user, but may be a hybrid of multiple users, machine assistedhumans, or even paired guides.

The problem of defining the context of a user is then translated to theproblem of finding a suitable reference-user or set of reference-users.In fact, the set of reference-users for a given user may have a highconsistency, and as known in the field of social networking. That is,assuming that the word “friend” is properly defined, the universe ofcontexts for a user may be initially estimated by the contexts ofinterest to his or her identified friends. Such an estimation technologyis best exploited in situations where error is tolerable, and whereleakage of user-specific contexts is acceptable.

In some cases, the reference-user sought is one with superior insights(rather than exemplary insights), that is, the reference-user is“better” than a normal user, and as such leads the other users. This isappropriate where an economic quality function is available, and themaximization of that function does not require significant compromise.This type of system has a well-defined and generally invariantdefinition of “best”, especially when an economic cost-benefit functioncan be defined and readily adopted.

In other cases, the reference-user should be the epitome of the class,and thus not quantitatively deviant from the group as a whole. In such acase, the user with the “best” insight might be considered statisticallydeviant from the mean, and therefore not a good choice for designationas reference-user.

For example, in a scientific literature database, an “expert” in a fieldmay be designated as a reference-user, and the context associated withthat expert representing the field of expertise. A database so organizedwould cluster the documents in the database around different spheres ofexpertise; the narrower the expertise of the designated expertreference-user, the higher the quality of distinctions which may bedrawn from other knowledge domains.

In contrast, a general purpose database such as Google may be used byfifth graders. The clustering of information based on expertise may leadto low ranking of documents appropriate for that type of user, and highranking of documents which are either incomprehensible to the user, orlacking relevance. Thus, the goal in a general purpose system is toidentify reference-users who are similarly situated to the actual user,and therefore whose usage correlates with the intended or supposed useby the user.

Indeed, these two different types of reference-users may both be used inparallel, though because they are not self-consistent as each represent“context”, these should be treated as independent or semi-independentfactors.

The “expert” reference-user, for example, may be of use to the fifthgrader; the reference-user profile can be used to distinguish variouscontexts at high levels, which can then be used to rank documents at theappropriate level for the user. The epitome reference-user may be usefulto the technical user; some relevant documents may be outside theexperience or sphere of the expert reference-user, and a more commonreference-user may provide useful insights for ranking or segregatingthose documents. By pairing the expert and the epitome, a comparison ofthe results may also be of use, especially in ranking the results interms of degree of specialization.

It may be useful to explicitly receive user profile information, orinferentially derive such information, in order to identify context. Inaddition to analyzing content associated with user actions, the speed,duration, and latency of user actions may be analyzed, as well as therange of contexts, and usage of content.

As a final note on the form of interaction of the reference user withthe data, in the typical case, we assume that the reference user canchoose how they filter, cluster and view the data set. Thus, in their“view”, a reference user may choose to subtract points they wish toview, or add points they wish to “view”. This process does not changethe dataset itself, but merely changes the way the reference userchooses to view the dataset. It changes the filter and is merelyreflective of their context.

Objects

It is therefore an object to provide a decision support system,comprising a user input port configured to receive user inputscomprising at least one user criterion and at least one user inputtuning parameter representing user tradeoff preferences for producing anoutput from a system which selectively produces an output of tagged datain dependence on the at least one user criterion, the at least one userinput tuning parameter, and a distance function; a reference-user inputconfigured to receive at least one reference-user input parameterrepresenting the at least one reference-user's analysis of the taggeddata and the corresponding user inputs, to adapt the distance functionin accordance with the reference-user inputs as a feedback signal,wherein the reference-user acts to optimize the distance function basedon the user inputs and the output, and on at least one reference-userinference; and an information repository configured to store the taggeddata.

It is a further object to provide a decision support system, comprisinga user input port configured to receive user inputs comprising at leastone user criterion and at least one user input tuning parameterrepresenting user tradeoff preferences for producing an output from asystem which selectively produces an output of tagged data in dependenceon the at least one user criterion, the at least one user input tuningparameter, and a distance function; a reference-user agent configured toreceive at least one reference-user input parameter representing the atleast one reference-user's analysis of the tagged data and thecorresponding user inputs, to adapt the distance function in accordancewith the user inputs as a feedback signal, wherein the reference-useragent acts to optimize the distance function based on the user inputsand the output, and on at least one reference-user inference derivedfrom at least one human user selected from a plurality of possible usersbased on an accuracy of selection according to an objective criterion;and an information repository configured to store the tagged data.

It is a still further object to provide a decision support method,comprising receiving user inputs comprising at least one user criterion,and at least one user input tuning parameter representing user tradeoffpreferences for producing an output; selectively producing an output oftagged data from a clustered database in dependence on the at least oneuser criterion, the at least one user input tuning parameter, and adistance function; receiving at least one reference-user input parameterrepresenting the at least one reference-user's analysis of the taggeddata and the corresponding user inputs, to adapt the distance functionin accordance with the reference-user inputs as a feedback signal; andclustering the database in dependence on at least the distance function,wherein the reference-user acts to optimize the distance function basedon the user inputs and the output, and on at least one reference-userinference.

The clustering may be automatically performed by a processor. Thedatabase may receive new data. The distance function may be applied tocluster the database including the new data before the at least onereference-user input parameter is received. The tagged data may comprisea valuation or rating. The distance function may be adaptive to newdata. The reference-user inference may represent at least one of avaluation and a validation. The user input tuning parameter may comprisea dimensionless quantitative variable that impacts a plurality of hiddendimensions. The hidden dimensions may comprise at least one ofcompleteness, timeliness, correctness, coverage, and confidence. Theuser input tuning parameter may balance completeness and correctness ofthe tagged data in the output.

Another object provides an information access method, comprisingreceiving a semantic user input comprising an indication of interest ininformation; determining a context of the user distinctly from thesemantic user input comprising an indication of interest in information;producing an output of at least tagged data from a clustered database independence on at least the user input, the determined context, and adistance function; monitoring a user interaction with the output; andmodifying the distance function in dependence on at least the monitoreduser interaction.

The method may further comprise selecting at least one commercialadvertisement extrinsic to the tagged data from the clustered databasefor presentation to the user, in dependence on at least: at least one ofthe semantic user input and the output of tagged data, and thedetermined context. The selecting may be further dependent on thedistance function. The monitoring may comprises monitoring a userinteraction with the at least one commercial advertisement, wherein thecommercial advertisement is selected in dependence on the distancefunction, and the distance function is modified based on the userinteraction with a selected advertisement. The method may furthercomprise reclustering the database in dependence on the modifieddistance function. The method may further comprise classifying aplurality of users, and distinguishing between difference classes ofusers with respect to the selection and modifying of respective ones ofa plurality of distance functions. The method may further comprisedetermining at least one reference-user from a set of users, based on atleast one fitness criterion, and selectively modifying the distancefunction dependent on a reference-user input in preference to anon-reference-user input. A user input is associated with a respectivereference-user in dependence on the context.

Another object provides an information processing method, comprising:clustering a database comprising a plurality of information recordsaccording to semantic information contained therein, wherein informationmay be classified in a plurality of different clusters in dependence ona context, such that a common semantic query to the database yieldsdifferent outputs over a range of contexts; producing an outputidentifying information records from the database in dependence on atleast a user semantic input, and a distance function; receiving userfeedback; and modifying at least one distance function in dependence onthe user feedback.

The method may further comprise determining a contextual ambiguity fromthe user semantic input, soliciting contextual ambiguity resolutioninformation from the user, and producing a follow-up output identifyinginformation records from the database in dependence on at least a usersemantic input, the contextual ambiguity resolution information, and atleast one distance function selected from a plurality of availabledistance functions in dependence on the contextual ambiguity resolutioninformation. The method may further comprise selecting at least onecommercial advertisement extrinsic to the information records in thedatabase for presentation to the user, in dependence on at least: theuser semantic input, and the contextual ambiguity resolutioninformation. The selecting may be further dependent on at least onedistance function. The method may further comprise selecting at leastone commercial advertisement extrinsic to the information records in thedatabase for presentation to the user, in dependence on at least: theuser semantic input, and the distance function. The monitoring maycomprise monitoring a user interaction with at least one commercialadvertisement presented to the user as part of the output, wherein thecommercial advertisement is selected in dependence on at least onedistance function, and the at least one distance function is modifiedbased on the user interaction with at least one selected advertisement.The method may further comprise reclustering the database in dependenceon the at least one modified distance function. The method may furthercomprise classifying a plurality of users, and distinguishing betweendifference classes of users with respect to the selection and modifyingof respective ones of a plurality of distance functions. The method mayfurther comprise assigning a reference-user status to at least one userwithin a set of users, based on at least one fitness criterion, andselectively weighting a user contribution to a modification of arespective distance function dependent on the reference-user status ofthe respective user. The reference-user status may be assigned withrespect to a context, and a user input is associated with a respectivedistance function in dependence on the context.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart according to a first embodiment of the technology;

FIG. 2 is a flowchart according to a second embodiment of thetechnology;

FIG. 3 is a flowchart according to a third embodiment of the technology;

FIG. 4 is a block diagram of a traditional computing systemarchitecture; and

FIG. 5 is a flowchart according to a fourth embodiment of thetechnology.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Search Engine

The reference-user is exploited in various ways. In a very uncommonscenario, the reference-user directly and in real time guides a searchresult. That is, the reference-user acts as expert assistance for theuser, a sort of reference librarian. The abilities of the reference-userare directly exploited, but this is expensive, non-scalable, and mayhave difficulty addressing contexts that transcend a singlereference-user, such as a hybrid context.

Another way to exploit a reference-user is to obtain a rich profile ofthe reference-user based on close monitoring of the reference-user orexplicit training by the reference-user of an automated system. Inessence, the reference-user's essence is transferred to an artificialagent, which then emulates the reference-user. This technique is bothscalable and relatively low cost, but may fail in new circumstances.That is, such systems may do a fair job at interpolation, but may havegreat difficulty extrapolating. Likewise, generalizations from thereference-user profile may be unhelpful, especially if thegeneralization transcends the reference-user scope.

A further way to exploit the reference-user is to proactively procure adecision from the reference-user, based on his or her inferences. Thesedecisions may be focused on defining cluster boundaries, which may betuned, for example, at the distance function, clustering criteria, oroptimization level, or imposed as an external constraint by directclassification (or reclassification) of one or more objects. Thus, aspart of a voluntary process, reference-users or potentialreference-users may be requested to provide feedback, or usage monitoredfor inferential feedback, to determine classifications (clustering) anda distance function representing quantitatively how much a given itemcorresponds to the putative classification. The clustering propertiesthus derived need not be used in a “social” context; that is, a user maybe able to identify the context as a cluster, without direct or indirectreference to the reference-user responsible or partially responsible forthe clustering. Therefore, a kind of collaborative filter may beimplemented, to identify the appropriate reference-user or affinitygroup, and thereafter exploit the identified reference-user or affinitygroup in various ways, as may be known, or described herein. In somecases, the distance function may have separate components for a value ofproper classification and a cost of misclassification. For example, in acommercial database, the retrieval cost is expensive, so there may be abias against inclusion of objects where the relevance is doubtful, ascompared to objects with equal distance, but more assured relevance.

This may be especially important where the reference-users have multipleroles, or where all roles are not relevant to a context. Thus, in asocial relationship system, the reference-user as a whole defines thecontext (which may significantly overlap for different reference-users),while in a clustered system, all that matters is the definition ofcluster boundaries and/or distance function, and the problem ofselecting a cluster is different than selecting a high affinityreference-user. However, not all context determination problems aredifficult, and therefore other statistical or artificial intelligencetechnologies may be employed.

In some cases, a pre-result may be provided to a user, which requestsuser input to select a particular context or contexts. This technique isdisfavored in a Google-type search engine, since typically, the userseeks high quality results in the first response, and consistency ofsearch results upon repetition. On the other hand, if a user submits aquery, the initial response may be context-free (or multi-context).However, the user may then be given various options for explicit orimplicit feedback, such that the ranking of results changes with eachnew page presented by the search engine. This feedback is a natural wayto receive input for defining reference-users and for obtaininginferences from reference-users or potential reference-users. Inaddition, there is typically insufficient time between submission of aninitial search and when the initial response is expected, in order toperform a computationally complex ad hoc clustering or other documentanalysis. However, the delay between the initial response (firstdownload) from a query and subsequent responses (downloads/pagerefreshes) may be sufficient to perform complex analytics on the subsetof responsive documents. Thus, according to one aspect, a databaseinterface is provided that implements an adaptive user interface basedon feedback received. In some cases, the feedback and context definitionmay be persistent, but in others, the context will be used only for theimmediately subsequent interactions.

It is noted that feedback from a reference-user in a Google type searchengine may be derived by monitoring click-throughs from the searchresults. A reference-user would presumably be able to filter usefulresults from those of limited value. The subset of results selected bythe reference-user represents a cluster, which can then be used as anexemplar for updating the clustering algorithm for future searcheswithin the cluster domain for which the reference-user is associated.

Thus, the first response from a database may be without defined context,or even specifically designed to elicit a definition of the context fromthe user. The second response may benefit from analytics as well asexplicit or implicit feedback from the user to define the context and/orcluster identification. In a typical massive database, results andpartial results are cached, and analytics may be performed on thesecached results to determine clusters of information according to variouscriteria. Given a user input seeking a database response, the databasemay initially reveal results representing different clusters thatcorrespond to the query. The user may than select one cluster whichincludes responses relevant to the context. The cluster selection isthen returned to the database system, which can then deliver resultsappropriate for that context. Note that the clusters initially presentedneed not directly correspond to the identified context. For example, ina complex semantic query, the cached clusters may represent distinctionsmade on a subset of the query, or using a fuzzy search algorithm. Oncethe actual cluster including relevant responses is identified, the querymay be re-executed using the formal user request, and the selectedrelevant responses. Typically, a massive database which provides realtime responses does not have sufficient time to perform iterativedatabase processes, while the “conversational” system can exploit userlatency to perform complex analytics and recursive processes.

Interactions of Reference-Users

In designating reference-users, it is sometimes useful to also designateanti-reference-users; that is, representatives of a class or contextthat is undesired, or those who demonstrate poor insights. Taking Googleagain as an example, much of the Internet includes sex and/or adultthemes, frivolous or trivial sites, and the like. However, these variouselements are not universally ignored, and therefore in the same way thatexperts on arcane academic topics can be identified, so can “experts” onInternet spam. By identifying these “experts”, a negative affinity maybe defined to help avoid undesired clusters or classes of informationfor a user. Thus, the reference-user does not necessarily trivialize theproblem to a single cluster with a universal distance function from acommon centroid for all objects. Rather, by providing multiplereference-users, the user's context can be matched with the bestreference-user (or hybrid/synthetic reference-user) which results in anoptimum of comprehensiveness and relevance for the given context. Moregenerally, the user need not select a singlecluster/classification/reference-user as the context, but rather acombination (e.g., linear combination) of variousclusters/classifications/reference-users may be employed. Asappropriate, negative weights, and required logical combinations (and,or, not, etc.) may be applied. In this way, the reference-user is notnecessarily an exclusive group with extraordinary capabilities, thoughin many cases, those types of reference-users are particularly valued.

This technology therefore has application as a filter againstinappropriate content, which may be able to implement fine distinctionsbetween acceptable and unacceptable content. In particular, an automatedfilter which is not guided by human decisions may have difficultydistinguishing “pornography” from “erotica”, while (according to JusticePotter Stewart), a reasonable human can make this distinctionintuitively. Thus, at risk of requiring the reference-users to actuallybehold the pornography in order to classify it, the distinctions may befinely drawn based on the human inference; note that the reference-useris typically monitored during normal activities, and not required toperform any particular activity. This example also raises the socialnetwork issue; since pornography is subject to community standards, thereference-user selected for this clustering/classification must berepresentative of the same community as is relevant to the user, andtherefore the same data may be subject to a plurality of clusterings anddistance functions. Similar distinctions may be drawn in variouscontexts—Darwinian evolutionists vs. creationists; conservatives vs.liberals; etc. The context may thus be independent of the database, andfor example relevant to an ideology of the user.

Assessments of Users

The present technology also provides education and assessments. That is,a normal user may be educated according to the insights of areference-user, and the ability of a user to classify similarly to anexemplary reference-user may be assessed. These technologies may ofcourse be integrated with other educational and assessment technologies.

Reference—Users in Asset Analysis

In the system and method according to the present technology, as appliedto investment data, a reference-user architecture is useful fordetermining peer groups among different funds, managers, segments. Inthis case, the goal is to select a reference-user who has demonstratedexemplary past performance at the task, and thus who likely has better“insight” into the investment quality. The reference-user(s) in thiscase are selected adaptively based on performance, and thus if a priorreference-user loses reliability, he is demoted. In general, thereference-user is not publicly designated, and has no knowledge that heor she serves as a reference-user, thus avoiding a subjective bias. Insome cases, a voting scheme may be employed, to provide a consensusamong reference-users. However, assuming that a reference-user does infact have superior capabilities, the voting or statistical averaging maysignificantly diminish the advantage of having a reference-user withsuperior insight; such users may be capable of reacting outside of thestatistical norms to great benefit, and therefore this advantage shouldnot be squandered by requiring that the reference-user conform tostatistical norms or models. Likewise, care should be employed whenexcluding outliers, since these may represent valuable opportunity.Whether to permit statistical deviation from the norm, or proceed byconsensus, is a design decision in the system.

According to another aspect of the technology, a large data set may beprocessed to define a reduced data set based on reliability and coverageof the data space. The goal is not to place every available data pointof the data set within the data space, but rather to define a filtereddata set with maximum reliable coverage. Thus, portions of the dataspace densely populated with high reliability data generally have ahigher threshold for inclusion of new data, while portions with lowerreliability or lower density more readily accept new data. In this way,reliable statistical inferences can be efficiently drawn, using feasibleanalysis. Metrics and algorithms are provided for representing therelative veracity and usefulness of individual instances of informationand the providing sources. The veracity of information is measured bythe difference, if any, between which it disagrees with an overall “bestestimate” calculated based on the preexisting data set. The usefulnessof information is measured by the amount by which the instance ofinformation decreases the amount of uncertainty. A reference-user mayinteract with this dataset to criteria regarding the density, veracityand usefulness criteria, influence data inclusion, and/or to cluster thedata within the set. In general, correctness is determined byengineering techniques such as total quality management (TQM) and TruthSeeking (triangulation) principles in continuous monitoring. Dataaccuracy needs to be measured not only at individual data point level,but also when calculating derivative data points. This technology may beused in an asset database system to permit investment analysis,portfolio analysis, and peer analysis, for example.

Based on this reduced data set, peer groups of multivariate data areautomatically determined using criteria relevant for humanconsideration, that is the data is projected into a low dimensionalcognitive space. The reduced data set may be supplemented with anoverlay of additional data (that is, similar data which is not in thereduced data set), which can then be subjected to the peer groupanalysis as well. The system is also appropriate for ad hoc queries,though common queries are pre-cached and thus results are available withlow latency. The peer clustering algorithms, and the reduced data setmay each be modified adaptively, providing dynamically updated analysistools. The system preferably supports queries relating to the data andrelationships of the data, as well as providing customizable dashboardsrepresenting common metrics of interest to the alternative investmentcommunity.

In order to automatically synthesize investment rating/grading ofobjects that represent investments, a distance function ortransformation function is generated off the data set. As the data setchanges, the distance function evolves such that all points with thesame net risk or risk reward profile, map to the same cluster or pointin the transformed space. The distance function is adaptive and “userevolvable”. This consists of a) a reference-user who trains the distancefunction b) a general group of users that continuously provide data andfeedback on its results. The automated risk report for a particularasset is generated by finding all assets that have a similar net risk,i.e., are the same distance radius distance from the investment riskpoint. This cluster of points may then be rank ordered according to thereturn metric. The rating is then the “alpha”, or excess return over theaverage representation of the cluster of similar points.

According to one aspect, a mapping algorithm maps the multivariatediscrete, continuous hybrid space representing the various factors thatdistinguish various risk reward profiles into a univariate normalizedspace such that it is now possible to apply asset allocation principles.

Intelligent Advertising

The value of an alternative asset (poorly valued because of aninefficient market) is the actually realized value at duration of thefinal exit for a party, as opposed to price, which is the transactionvalue attributed at the trade or transaction today. When we use this inthe context of digital assets such as domain names, Google rankings, adplacement etc. all of which classify as alternatives because they aretraded in an inefficient market, then the price is the price paid by theadvertiser. If the search engine makes its advertising placementdecision based on the highest advertising price only, over the long termthis results in poorer placement of items and attrition of eyeballs, ineffect reducing the value of the asset. Thus, understanding thedifference between price and value, even directionally is critical.Accordingly, another aspect of the technology is to optimizeadvertisement placement into a natural result (that is, not influencedby the advertising) by referring to the clustering of the data as wellas the context, such that the advertising is appropriate, inoffensive,and likely to increase the overall value of the enterprise, based onboth the short term revenues from advertising, and the long termreputation and future cash flows that may be influenced. For example, aninappropriately placed ad will generate advertising revenue, but maydisincentivize the advertiser to place ads in the future. Anappropriately placed ad, which is contextually appropriate and topicallyappropriate, is more likely to result in a consumer-advertisertransaction, and thus lead to higher future advertising revenues, evenif the present value of the ad is not the highest possible option.

A reference-user in this context may be a user who transacts with anadvertiser. By matching users with a reference-user, within theappropriate context, it is more likely that the users will also transactwith that advertiser, as compared to users in a different context. Theads may therefore be clustered as artificial insertions into the datauniverse, and clustered accordingly. When a user's correspondingreference-user(s) and cluster(s) of interest are identified, theadvertisements within those clusters may then be considered for deliveryto the user.

Location-Context Search

According to an embodiment of the technology, location may be used as acontext to define a reference-user, and the reference-user profile isthen exploited to produce a system response. Thus, rather thaniteratively or implicitly determining a relevant context for a user, alocation cue, such as GPS location, Internet service provider (ISP)location, IP mapping, inverse domain name lookup, ZIP code, userdemographic profile, or the like. The location may this be the presentlocation or a reference location.

The location context is actually determined by the respective usersthemselves both for the current and the reference location. A particularuser has a particular set of location contexts, e.g., given an ambiguouslocation such as “School Street”, a first user may have the referencelocation context as “School Street in Providence R.I., USA” where thefirst user's relative lives versus a second user who may have thereference location context as “School Street in Belmont, Mass., USA”where the second user's child goes to school. Both reference locationsare contextually relevant to the particular users, but different betweendifferent users.

Based on the context, e.g., location, a data entry or response may beselectively processed. Thus, a New Yorker may use language in adifferent way than a Londoner. In order to interpret the language,profiles of reference-users with similar location references, i.e.,selected based on the context, are analyzed for query responsecorrespondence. For example, the reference-user profiles may be used toperform word translations or equivalencies, rank search results, selecttypes of results, and the like. As an example, the first user'sreference location is also more relevant to other users/reference userin the first user's cluster.

Once the meaning of the input is determined with some reliability, thenext step is determining a useful output. Note that the context forinterpretation of the input may differ from the context for producing ameaningful output; that is, the relevant reference-users need not be thesame. For example, the New Yorker in London might seek, through a speechrecognition system on a smartphone, a club for entertainment. Uponrecognizing both location cues, i.e., the origin of the user (which maybe accessible from a telephone number or ID, user profile, cookie,speech accent, etc.) and the current location of the user, a set ofreference-users may be selected. These may include other New Yorkers inLondon who visit clubs. However, the set of reference-users is not solimited. The reference-users may be selected more broadly based onpreferences, affinities, chronologies, and the like, and may includeboth visitors to London and natives. Using location tracking ande-commerce technology, information about what day a respectivereference-user went to the club, how long her or she spent, what theyordered, how much they tipped, etc., may all be available information.This type of information may be correlated with the user's past history,inside and out of London. Of course, to the extent that explicit ratingsof clubs are available, these may also be exploited, but these explicitratings tend to display bias and are not statistically normalized orvalidated. Note that the reliability of explicit ratings may improvedramatically when broken down by context, e.g., the reference-user(s)responsible for the rating. In general, using a large body of availableinformation for prospective reference-users, a cluster analysis isperformed which may rank or weight different users with respect to theirprobative value toward resolving the query. Depending on the systemimplementation, some aspects of the cluster analysis may be performed inadvance, and thus only final stages computer on demand. Thus, thecontext for generating the system response may be determined, and thatcontext used to determine the cluster in which the user “resides”, whichthen defines the reference-user(s) to be referenced in formulating theresponse. Alternately, an affinity with a reference user or user(s) isdetermined, e.g., with a collaborative filter, and that set ofreference-users used to determine the context cluster. In either case,the response is then generated based on the context cluster, which isstatistically linked to the reference-users associated with thatcluster. The favorite clubs for the reference-users are then presentedas a response to the query, and may be ranked according to weightingsderived from the analysis.

It is noted that systems of the type described are typically subsidizedby advertising. Therefore, once the meaning of the query is determined,e.g., the user is looking for a club, a set of ads for clubs, clubgoers, or the user abstract from his goal directed activity, may bepresented. In general, a club would not wish to solicit a patron whowould not have fun; the tab and tip will be low, and future referralsabsent. Likewise, a targeted ad of this type may be relativelyexpensive, and thus there would be incentive for advertisers to presentads that will likely yield return on investment. The system itself hasas a goal to deliver relevant advertising, since user mistrust in theoutput will lead to loss of usage and advertising revenues. Given thegenerally aligned incentives, therefore, the advertisers themselves maybe provide useful and rich context information. That is, in contrast tonormal users, who will often not spend time training a third partysystem, advertisers may be willing to spend considerable time definingtheir preferred customers, and providing useful information for thosecustomers. In cases where there is an incentive to “cheat”, that is,game the system to achieve an unnatural result, feedback from actualusers and a system-imposed punishment may be effective. Thus, if a useris “pushed” to go to a club they do not enjoy, the user may end up beinga bad customer (low tab and tip), and may help redefine the cluster sothat user for which he or she becomes a reference-user have reducedlikelihood of becoming patrons. Since the system may be quiteinteractive and ubiquitous, feedback may be in nearly real-time. Ofcourse, permitting advertisers to feed the system with information ismerely optional, and therefore to the extent that some users seek totaint the system, the cluster analysis and context sensitivity mayexclude other users from adverse impact.

Advertisers can target the most contextually relevant reference andcurrent location to push particular content to a respective user.

Recommendation Engine

In another embodiment, a user seeks a recommendation from arecommendation engine. The recommendation engine containsidentifications and profiles of users who have postedrecommendations/ratings, as well as profiles for users and usagefeedback for the system. A user seeking to use the engine is presented(at some time) with a set of questions or the system otherwise obtainsdata inputs defining the characteristics of the user. In this case, theuser characteristics generally define the context which is used tointerpret or modify the basic goal of the user, and therefore thereference-user(s) for the user, though the user may also define ormodify the context at the time of use. Thus, for example, a user seeksto buy a point-and-shoot camera as a gift for a friend. In this case,there are at least four different contexts to be considered: the gift,the gift giver, the gift receiver, and the gifting occasion. Thelikelihood of finding a single reference-user appropriate for each ofthese contexts is low, so a synthetic reference-user may be created,i.e., information from multiple users and gifts processed and exploited.The issues for consideration are; what kinds of cameras have peoplesimilarly situated to the gift giver (the user, in this case) had goodexperiences giving? For the recipient, what kinds of cameras do similarrecipients like to receive? Based on the occasion, some givers andrecipients may be filtered. Price may or may not be considered anindependent context, or a modifier to the other contexts. The variousconsiderations are used in a cluster analysis, in which recommendationsrelevant to the contexts may be presented, with a ranking according tothe distance function from the “cluster definition”. As discussed above,once the clustering is determined, advertisements may be selected asappropriate for the cluster, to provide a subsidy for operation of thesystem, and also to provide relevant information for the user aboutavailable products.

Once again, the context is specific to the particular user and thus theright kind of camera for a first user to give a friend is not the sameas the right kind of camera for a second user to give to a differentfriend; indeed, even if the friend is the same, the “right” kind ofcamera may differ between the two users. For example, if the first useris wealthier or other context differences.

Embodiments

One embodiment provides a decision support system, corresponding to themethod shown in FIG. 2. A user input port receives user inputs, whichdefine a user criterion or criteria, and also at least one user inputtuning parameter. This parameter represents user tradeoff preferencesfor producing an output from a system 201. The output is may be in theform of tagged data, selected in dependence on the at least one usercriterion, the at least one user input tuning parameter, and a distancefunction 202. A reference-user input is also provided which receives oneor more reference-user input parameters representing a respectivereference-user's analysis of the tagged data and the corresponding userinputs 203. The reference-user input is used to adapt the distancefunction in accordance, using the reference-user inputs as a feedbacksignal. The reference-user thus acts to optimize the distance functionbased on the user inputs and the output, and on at least onereference-user inference. This inference may be derived from at leastone human user selected from a plurality of users based on an accuracyof selection according to an objective criterion 204. An informationrepository 205, such as a structured query language database, orso-called “No-SQL” database, configured to store the tagged data.

Another embodiment provides a decision support system, also generallycorresponding to the method shown in FIG. 2, having a user input portconfigured to receive user inputs including at least one user criterionand at least one user input tuning parameter representing user tradeoffpreferences 201. The user inputs are used to produce an output of taggeddata in dependence on the at least one user criterion, the at least oneuser input tuning parameter, and a distance function 202. Areference-user agent is provided, which is configured to receive atleast one reference-user input parameter representing the at least onereference-user's analysis of the tagged data and the corresponding userinputs 203. The reference-user agent selectively adapts the distancefunction in accordance with the user inputs as a feedback signal. Thatis, the user inputs are not necessarily directly used to providefeedback, but rather are filtered through the reference-user agent. Thereference-user agent acts to optimize the distance function based on theuser inputs and the output, and on at least one reference-user inferencederived from at least one human user selected from a plurality ofpossible users based on an accuracy of selection according to anobjective criterion 204. An information repository is provided,configured to store the tagged data 205.

A further embodiment provides a decision support method represented inFIG. 1, comprising receiving user inputs comprising at least one usercriterion, and at least one user input tuning parameter representinguser tradeoff preferences for producing an output 101; selectivelyproducing an output of tagged data from a clustered database withdependence on at least one user criterion, the at least one user inputtuning parameter, and a distance function 102; receiving at least onereference-user input parameter representing the at least onereference-user's analysis of the tagged data and the corresponding userinputs, to adapt the distance function in accordance with thereference-user inputs as a feedback signal 103; and clustering thedatabase in dependence on at least the distance function 104, whereinthe reference-user acts to optimize the distance function based on theuser inputs and the output, and on at least one reference-user inference105. The clustering may be automatically performed by a processor. Thedatabase may receive new data. The distance function may be applied tocluster the database including the new data before the at least onereference-user input parameter is received. The tagged data may comprisea valuation or rating. The distance function may be adaptive to newdata. The reference-user inference may represent at least one of avaluation and a validation. The user input tuning parameter may comprisea dimensionless quantitative variable that impacts a plurality of hiddendimensions. The hidden dimensions may comprise at least one ofcompleteness, timeliness, correctness, coverage, and confidence. Theuser input tuning parameter may balance completeness and correctness ofthe tagged data in the output.

Another embodiment provides an information access method, as shown inFIG. 3, comprising receiving a semantic user input comprising anindication of interest in information 301; determining a context of theuser distinctly from the semantic user input comprising an indication ofinterest in information 302; producing an output of at least tagged datafrom a clustered database in dependence on at least the user input, thedetermined context, and a distance function 303; monitoring a userinteraction with the output 304; and modifying the distance function independence on at least the monitored user interaction 305. The methodmay further comprise selecting at least one commercial advertisementextrinsic to the tagged data from the clustered database forpresentation to the user, in dependence on at least: at least one of thesemantic user input and the output of tagged data, and the determinedcontext 306. The selecting may be further dependent on the distancefunction. The monitoring may comprises monitoring a user interactionwith the at least one commercial advertisement, wherein the commercialadvertisement is selected in dependence on the distance function, andthe distance function is modified based on the user interaction with aselected advertisement 307. The method may further comprise reclusteringthe database in dependence on the modified distance function. The methodmay further comprise classifying a plurality of users, anddistinguishing between difference classes of users with respect to theselection and modifying of respective ones of a plurality of distancefunctions 308. The method may further comprise determining at least onereference-user from a set of users, based on at least one fitnesscriterion, and selectively modifying the distance function dependent ona reference-user input in preference to a non-reference-user input 309.A user input is associated with a respective reference-user independence on the context 310.

Another embodiment provides an information processing method, as shownin FIG. 5, comprising: clustering a database comprising a plurality ofinformation records according to semantic information contained therein,wherein information may be classified in a plurality of differentclusters in dependence on a context, such that a common semantic queryto the database yields different outputs over a range of contexts 501;producing an output identifying information records from the database independence on at least a user semantic input, and a distance function502; receiving user feedback 503; and modifying at least one distancefunction in dependence on the user feedback 504. The method may furthercomprise determining a contextual ambiguity from the user semanticinput, soliciting contextual ambiguity resolution information from theuser, and producing a follow-up output identifying information recordsfrom the database in dependence on at least a user semantic input, thecontextual ambiguity resolution information, and at least one distancefunction selected from a plurality of available distance functions independence on the contextual ambiguity resolution information 505. Themethod may further comprise selecting at least one commercialadvertisement extrinsic to the information records in the database forpresentation to the user, in dependence on at least: the user semanticinput, and the contextual ambiguity resolution information 506. Theselecting may be further dependent on at least one distance function.The method may further comprise selecting at least one commercialadvertisement extrinsic to the information records in the database forpresentation to the user, in dependence on at least: the user semanticinput, and the distance function 507. The monitoring may comprisemonitoring a user interaction with at least one commercial advertisementpresented to the user as part of the output, wherein the commercialadvertisement is selected in dependence on at least one distancefunction, and the at least one distance function is modified based onthe user interaction with at least one selected advertisement 508. Themethod may further comprise reclustering the database in dependence onthe at least one modified distance function 509. The method may furthercomprise classifying a plurality of users, and distinguishing betweendifference classes of users with respect to the selection and modifyingof respective ones of a plurality of distance functions 510. The methodmay further comprise assigning a reference-user status to at least oneuser within a set of users, based on at least one fitness criterion, andselectively weighting a user contribution to a modification of arespective distance function dependent on the reference-user status ofthe respective user 511. The reference-user status may be assigned withrespect to a context, and a user input is associated with a respectivedistance function in dependence on the context 512.

Hardware Overview

FIG. 4 (see U.S. Pat. No. 7,702,660, issued to Chan, expresslyincorporated herein by reference), shows a block diagram thatillustrates a computer system 400 upon which an embodiment of theinvention may be implemented. Computer system 400 includes a bus 402 orother communication mechanism for communicating information, and aprocessor 404 coupled with bus 402 for processing information. Computersystem 400 also include a main memory 406, such as a random accessmemory (RAM) or other dynamic storage device, coupled to bus 402 forstoring information and instructions to be executed by processor 404.Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Computer system 400 further may also includea read only memory (ROM) 408 or other static storage device coupled tobus 402 for storing static information and instructions for processor404. A storage device 410, such as a magnetic disk or optical disk, isprovided and coupled to bus 402 for storing information andinstructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

The invention is related to the use of computer system 400 forimplementing the techniques described herein. According to oneembodiment of the invention, those techniques are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from anothermachine-readable medium, such as storage device 410. Execution of thesequences of instructions contained in main memory 406 causes processor404 to perform the process steps described herein. In alternativeembodiments, hard-wired circuitry may be used in place of or incombination with software instructions to implement the invention. Thus,embodiments of the invention are not limited to any specific combinationof hardware circuitry and software.

The term “machine-readable medium” as used herein refers to any mediumthat participates in providing data that causes a machine to operationin a specific fashion. In an embodiment implemented using computersystem 400, various machine-readable media are involved, for example, inproviding instructions to processor 404 for execution. Such a medium maytake many forms, including but not limited to, non-volatile media,volatile media, and transmission media. Non-volatile media includes, forexample, optical or magnetic disks, such as storage device 410. Volatilemedia includes dynamic memory, such as main memory 406. Transmissionmedia includes coaxial cables, copper wire and fiber optics, includingthe wires that comprise bus 402. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infra-red data communications. All such media must betangible to enable the instructions carried by the media to be detectedby a physical mechanism that reads the instructions into a machine.Non-transitory information is stored as instructions or controlinformation.

Common forms of machine-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, or any other magneticmedium, a CD-ROM, any other optical medium, punchcards, papertape, anyother physical medium with patterns of holes, a RAM, a PROM, and EPROM,a FLASH-EPROM, any other memory chip or cartridge, a carrier wave asdescribed hereinafter, or any other medium from which a computer canread.

Various forms of machine-readable media may be involved in carrying oneor more sequences of one or more instructions to processor 404 forexecution. For example, the instructions may initially be carried on amagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anIntegrated Services Digital Network (ISDN) card or a modem to provide adata communication connection to a corresponding type of telephone line.As another example, communication interface 418 may be a local areanetwork (LAN) card to provide a data communication connection to acompatible LAN. Wireless links may also be implemented. In any suchimplementation, communication interface 418 sends and receiveselectrical, electromagnetic or optical signals that carry digital datastreams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are exemplary forms of carrier wavestransporting the information.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

In this description, several preferred embodiments were discussed.Persons skilled in the art will, undoubtedly, have other ideas as to howthe systems and methods described herein may be used. It is understoodthat this broad invention is not limited to the embodiments discussedherein. Rather, the invention is limited only by the following claims.

The invention may be used as a method, system or apparatus, asprogramming codes for performing the stated functions and theirequivalents on programmable machines, and the like. The aspects of theinvention are intended to be separable, and may be implemented incombination, subcombination, and with various permutations ofembodiments. Therefore, the various disclosure herein, including thatwhich is represented by acknowledged prior art, may be combined,subcombined and permuted in accordance with the teachings hereof,without departing from the spirit and scope of the invention.

What is claimed is:
 1. A method for selecting an object from a group ofmultidimensional objects, comprising: receiving inputs with respect tothe group of multidimensional objects comprising hidden dimensions froma plurality of sources; receiving at least one tuning parameter thatimpacts at least a hidden dimension of the group of multidimensionalobjects; generating a subset of multidimensional objects from the groupof multidimensional objects based on at least one selection criterion,the subset of multidimensional objects having fewer multidimensionalobjects than the group of multidimensional objects; generating aquantitative analysis of the subset of multidimensional objects with atleast one automated processor, based on: respective inputs received fromthe subset of sources having fewer sources than the plurality ofsources, characteristics of a respective source from which therespective inputs are received, the at least one tuning parameter, andthe at least one selection criterion, using a multidimensional distancefunction dependent on at least the hidden dimensions to reduce a numberof dimensions of the group of multidimensional objects to form clusters;automatically selecting at least one object based on at least thequantitative analysis and the clusters; receiving feedback relating tothe at least one selected object; and automatically updating acharacteristic of the respective source based on the feedback.
 2. Themethod according to claim 1, wherein the quantitative analysis comprisesperforming a clustering of the group of multidimensional objects.
 3. Themethod according to claim 1, wherein the quantitative analysis comprisesperforming a clustering selectively based on the subset ofmultidimensional objects.
 4. The method according to claim 1, whereinthe multidimensional distance function is optimized according to atleast one optimization criterion.
 5. The method according to claim 1,wherein the at least one selected object comprises a proper subset ofclusters within a cluster space of the group of multidimensional objectsaccording to the multidimensional distance function.
 6. The methodaccording to claim 1, wherein the characteristics of the respectivesource from which the respective inputs are received comprise acorrectness of at least one prior input from the respective sourcerepresenting a prediction of an outcome.
 7. The method according toclaim 1, wherein the received inputs comprise at least one of aninference, a recommendation, a prediction, and a tradeoff preference. 8.The method according to claim 1, wherein the group of multidimensionalobjects comprise semantic documents having a plurality of words, havinga respective number of dimensions relating the plurality of words. 9.The method according to claim 1, wherein the at least one selectioncriterion comprises a database query of the group of multidimensionalobjects.
 10. The method according to claim 1, wherein the group ofmultidimensional objects comprise contextual ambiguity, furthercomprising context information to resolve the contextual ambiguity. 11.The method according to claim 1, wherein the subset is a single source,selected based on a competition between sources.
 12. The methodaccording to claim 1, further comprising receiving feedback relating toan input from a respective source, and automatically updating acharacteristic of the respective source based on the feedback with theat least one automated processor.
 13. A method for selecting at leastone object from a plurality of multidimensional objects, comprising:receiving inputs with respect to the plurality of multidimensionalobjects comprising hidden dimensions from a plurality of sources;receiving at least one tuning parameter that impacts at least one hiddendimension of the plurality of multidimensional objects; generating asubset of multidimensional objects from the plurality ofmultidimensional objects based on at least one selection criterion, thesubset of multidimensional objects having fewer multidimensional objectsthan the plurality of multidimensional objects; generating aquantitative analysis of the subset of multidimensional objects, basedon: the respective inputs received from the subset of the plurality ofsources having fewer sources than the plurality of sources,characteristics of the respective source from which the respectiveinputs are received, the at least one tuning parameter; and the at leastone selection criterion; using a multidimensional distance function toreduce a number of dimensions of the plurality of multidimensionalobjects by forming clusters; automatically selecting at least one objectas an optimum object, based on at least the quantitative analysis andthe clusters; receiving feedback relating to the at least one selectedobject; and automatically updating a characteristic of the respectivesource based on the feedback.
 14. The method according to claim 13,wherein the quantitative analysis comprises clustering the plurality ofmultidimensional objects or the subset of multidimensional objects. 15.The method according to claim 13, wherein the at least one selectedobject comprises a proper subset of clusters within a cluster space ofthe plurality of multidimensional objects according to themultidimensional distance function.
 16. The method according to claim13, wherein the characteristics of the respective source comprise acorrectness of at least one prior input from the respective sourcerepresenting a prediction of an outcome associated with the at least oneselected object.
 17. The method according to claim 13, furthercomprising receiving feedback relating to an input from a respectivesource, and automatically updating a characteristic of the respectivesource based on the feedback.
 18. A system for selecting an object froma group of multidimensional objects, comprising: an input portconfigured to: receive inputs with respect to the group ofmultidimensional objects from a plurality of sources, each object havinga plurality of hidden dimensions; receive at least one tuning parameterthat impacts at least one hidden dimension of the group ofmultidimensional objects; at least one automated processor, configuredto: generate a subset of multidimensional objects from the group ofmultidimensional objects based on at least one selection criterion, thesubset of multidimensional objects having fewer multidimensional objectsthan the group of multidimensional objects; generate a quantitativeanalysis of the subset of multidimensional objects, based on: therespective inputs received from the subset of the plurality of sourceshaving fewer sources than the plurality of sources, characteristics of arespective source of the subset of the plurality of sources from which arespective input is received, at least one tuning parameter; and atleast one selection criterion, use a multidimensional distance functionto reduce a number of dimensions of the group of multidimensionalobjects to form clusters, and to select at least one object based on atleast the quantitative analysis and clustering; at least one output portconfigured to convey data selectively associated with the at least oneselected object; and the input port being configured to receive feedbackrelating to the at least one selected object, wherein the at least oneautomated processor is further configured to automatically update acharacteristic of the respective source based on the feedback.
 19. Thesystem according to claim 18, wherein the input port is furtherconfigured to receive context data, wherein the at least one automatedprocessor is further configured to quantitatively analyze and clusterthe group of multidimensional objects to select the object based on atleast the context data.
 20. The system according to claim 18, furthercomprising an input port configured to receive feedback relating to theselected at least one object, wherein the at least one automatedprocessor is further configured to automatically update a characteristicof the respective source based on the feedback.